In this approach to image fusion,
the fusion task is expressed as an bayesian optimization problem.

Using the multisensor image data and an a-prori model of the fusion result,
the goal is to find the fused image which maximizes the a-posteriori probability.
Due to the fact that this problem cannot be solved in general,
some simplifications are introduced: All input images are modeled
as markov random fields to define an energy function which describes the fusion goal.
Due to the equivalence of of gibbs random fields and markov random fields,
this energy function can be expressed as a sum of so-called
clique potentials, where only pixels in a predefined neighborhood affect the actual pixel.

The fusion task then consists of a maximization of the energy function.
Since this energy function will be non-convex in general,
typically stochastic optimization procedures such as
simulated annealing or modifications like iterated conditional modes will be used.